Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data

With the spread of the deadly coronavirus disease throughout the geographies of the globe, expertise from every field has been sought to fight the impact of the virus. The use of Artificial Intelligence (AI), especially, has been the center of attention due to its capability to produce trustworthy results in a reasonable time. As a result, AI centric based research on coronavirus (or COVID-19) has been receiving growing attention from different domains ranging from medicine, virology, and psychiatry etc. We present this comprehensive study that closely monitors the impact of the pandemic on global research activities related exclusively to AI. In this article, we produce highly informative insights pertaining to publications, such as the best articles, research areas, most productive and influential journals, authors, and institutions. Studies are made on top 50 most cited articles to identify the most influential AI subcategories. We also study the outcome of research from different geographic areas while identifying the research collaborations that have had an impact. This study also compares the outcome of research from the different countries around the globe and produces insights on the same.


Introduction
The omnipresence of Artificial Intelligence (AI) within the last decade is magnificent considering its applicability in almost all the real-world contexts. AI is powered by data science and exploratory analysis (DS/DEA), machine learning (ML) and deep learning (DL) approaches, which have shown significant improvements in several domains such as cyber security [47], cancer treatment [10], clean energy [54], financial sector [29], global education [45], etc. Healthcare and medical diagnosis are the other crucial areas where AI has shown potential in analyzing big data sets. Whether it is analytics on patient information to provide accurate predictive analysis or discovering new drugs to cure novel diseases in a timely manner, AI has come a very long way. To visualize the overall picture, Fig. 1 sums up various sectors in the medical domain, where AI has brought revolution and has increased the efficiency of the outcomes. These classifications are just representative to provide the perspective of the effectiveness of AI in these diverse areas.
Such effectiveness of AI can be seen in assisting scientists in tackling the recent pandemic caused by the coronavirus also termed as COVID-19. As of August 15, 2021, a total of 201 million [86] cases of the novel coronavirus disease have been registered globally since the first case of the same was reported from China in December 2019 [82]. This figure of affected cases worldwide is a clear indication of how deadly the novel COVID-19 is. Due to the widespread impact of the virus, research activity related to COVID-19 has been of central importance to many researchers worldwide, in the hope of discovering a new drug to cure the disease, to obtain trends of active or affected cases, or to predict the occurrence of the disease in individuals. Since there was no immediate cure to this virus, the number of fatalities increased gradually worldwide. It was hence necessary to target this devastating pervasion with state-of-the-art AI tools that could help fight the battle with COVID-19. The computational and precise predictions from AI approaches are quite evident, as can be seen from the results in various real-world applications [32,33,62,71,74,36].
Keeping an eye on this boost in AI centric based research on COVID-19, it is crucial to devise a systematic methodology to identify the sources of the most impactful research. With this paper, we aim to construct a bridge between the publications and interested researchers Fig. 1 Applicative areas of AI in healthcare and medical diagnosis who would like to identify the most impactful research content as well as related information. Furthermore, a benchmark of current scientific publications would also be created through such a study, leading to more interest from researchers in various fields belonging to diverse institutions and geographies converging on this area.
Scientometric (also refereed as Bibliometric) analysis provides a systemic review format for studying research publications within a scientific domain. This includes analyses of the citation structure, best papers based on citation count, research areas, highly productive and influential journals, impactful authors, co-citation amongst authors, institutional participation, geographic contribution, bibliographic coupling amongst authors, countries, institutions, as well as that of journals etc. Such analyses have been performed on multiple domains till date, such as a journal specific study of publication and citations structure [57,61,95]. In addition to these, there are studies which provided the results of a scientometric analysis performed to gain insight into research areas such as the study of aggregation operators [11], industry 4.0 revolution [58], brain MRI research [18], multimedia big data [43], etc. Similarly in the medical domain, the scientific outputs were tracked within the topics of the Ebola outbreak [93], global malaria vaccine [35], and Dengue research [96]. A study akin to these was also performed in [3], pertaining to the Zika virus outbreak.
Some researchers in the past studied the research landscape related to COVID-19. In [67], Sahoo and Pandey evaluated the research performance of the overall pandemic due to COVID-19, based on scientometric indicators, where the literature data was obtained from Scopus. Colavizza et al. [25] also recently produced a scientometric overview of the CORD-19 dataset, which is regularly updated with medical related articles from Medline, PubMed, the WHO database, arXiv, bioRxiv and medRxiv. Authors hence presents a detailed literature study based on medical literature and the related dataset. Another such analysis was performed in [37] where in addition to a scientometric study, safety related research dimensions were also presented through a scoping review, which identified the different types of safety issues that attracted research. Similarly, scientometric analyses were also performed in [2,52], focusing on research related to the coronaviruses from 1900 to 2020 and the overall statistics of the global research output respectively. Cunningham et al. [28] produced results from a scientometric analysis with the main aim of studying collaborations in the time of COVID-19. Apart from these, some researchers also considered the impact and hence-forth, the interest of COVID-19 research on specific areas such as endocrinology [7] and ophthalmology [44].
The literature also finds some traces of bibliometric/scientometric studies targeted on AI or related domains. Islam et al. [40] studies the initial bibliometric analysis of COVID-19 on AI. They included only 729 papers with only quantitative analysis of the data gathered from Web of Science (WoS). In a similar study, Wu et al. [89], studied about 1903 articles with the emphasis on visual knowledge map analysis. Abumalloh et al. [1] presented the systematic literature review of the computational approaches of medical image processing specific to the COVID-19. The study by Chicaiza et al. [20] covered the bibliometric analysis of DL related works only for the year 2020. Rodríguez-Rodríguez et al. [66] performed the scientometric analysis of the AI, ML, big data, and Internet of Things (IoT) approaches with applications on the pandemic. In this paper, we have obtained an in-depth knowledge about the current structure of publications related to AI centric research on COVID-19. A bibliometric analysis is presented of the scientific publication data available on the WoS database [24]. WoS is accredited with high-quality publications from high ranked journals and international conferences. The factors such as citation counts, number of publications, and their document types are considered. Furthermore, data related to the highly cited research on AI for COVID-19, along with research areas, journals, institutions, authors, countries etc. are mined in great depth to obtain useful insights. Performing such analysis leads to some interesting results about the research focused on applications of AI in COVID-19.
Collectively, the main purpose of devising this study is to empirically approach a view of how AI backed research related to COVID-19 has been produced to become impactful. Such a study can be helpful in multiple ways. Major contributions of this article have been compiled below: 1. The research landscape related to AI approaches for COVID-19 is extensively studied. 2. This study provides a quantitative and qualitative analysis of the AI centric scientific research on COVID-19. 3. Different parameters of the recent publications are extracted and studied to explore the landscape of this research area. These are: country-wise analysis, institution-wise analysis, source analysis, productive and influential authors, influential AI subcategories etc. 4. A bibliographic coupling of various parameters and keyword analysis is presented for deeper understanding of the targeted research area. 5. This work provides a platform for future research that can directly build upon the existing literature by referring to the current research trend on AI for COVID-19. 6. The research outcome of the produced scientometric analysis explicitly depicts the overall interest of researchers in the topic of utilizing AI in COVID-19 research. 7. Specifically, Table 3 can be of utmost importance for any new researcher trying to foray into the research of COVID-19, by leveraging the information of the most recent as well as trending work within the area.
Rest of the paper is organized as follows: Section 2 provides a background to the COVID-19 outbreak, while giving a bird's eye view of the related AI centric research. In Section 3, we discuss the technology utilized to perform the analysis and visualize the data effectively. In the subsequent section, the methodology is laid out followed by the analysis and knowledge mining. The paper is concluded in Section 5.

Background
Followed by multiple major attacks from viruses such as the severe acute respiratory syndrome coronavirus (SARS-COV), the H1N1 influenza, and the Middle East respiratory syndrome coronavirus (MERS-COV) [15], the world was grappled by the COVID-19 with its first case discovered in late 2019. Growing from this, the virus spread rapidly through geographies. This spread was officially recognized by WHO first [82] on January 21, 2020, followed by declaring it a global health emergency [83]. Finally, through its 40th Situation Report, WHO declared the outbreak as a global pandemic on March 11, 2020 [84,85]. Fast forward to August 2021, confirmed cases of COVID-19 infected millions across various regions of the globe. This increased motivated researchers from various disciplines to come together to study and develop models that would help in curbing the spread of the deadly virus. This motivation was found to be especially true for the AI research community, due to the evident success of AI within diverse fields. While there were 945 papers published in 2020 with citation count of 3,859, the year 2021 had already seen the publication of 1,491 papers with 11,748 citations. With this, it is evident that interest in the AI centric research on COVID-19 will gradually increase as time progresses, and it is likely to grow over time.
Notably, many research works on this topic are already scheduled to be published in the future months.
The study and analysis produced in this paper stands out from all the previous scientometric analyses mentioned in the previous section due to the fact that this work is based on the study of AI-centric research on COVID-19. Apart from this, the scientometric review performed here is extensive in the domain considered, spanning multiple concepts such as trending literature, overall citations, research areas, journals, authors, institutions, countries, keywords and topics, funding agencies, etc. Additionally, an in depth review of the top research articles is performed manually to study the actual use of AI within these works. These articles are then bifurcated into various sub-categories and fields of AI, revealing the interest of researchers, as well as readers in particular contributions of AI. Such a comprehensive study is not visible in any of the existing scientometric analyses presented above.

Technology utilized
To conduct the investigation on publications related to COVID-19, many technical tools were utilized. We briefly discuss them here.
1. Web of Science (WoS): Maintained by Clarivate Analytics, WoS [24] is a website which hosts multiple databases pertaining to diverse academic disciplines. These databases provide comprehensive data about the citation structure as well as other meta-data about the query presented to the database. In this work, WoS has been queried upon to obtain relevant publication information about COVID-19. 2. VOSviewer: It is a software tool [31] utilized for the development of bibliometric networks including multiple entities. These entities can range from being institutions, authors, countries, keywords, journals etc. A comprehensive structure amongst these entities is hence viewed, made possible by VOSviewer. However, there are cases in which the graphs generated by VOSviewer do not have some nodes labeled. Since the overall knowledge gained through such structures is quite good, we ignored the aforementioned drawback and utilized this tool for bibliometric visualizations. 3. Python: The rest of the figures in this paper have been drawn with the help of Python 3.6.
The bar plots, line plots and frequency histograms have been plotted utilizing the Pandas, Matplotlib, and Numpy modules, as a part of the exploratory data analysis performed.
The details of the scientific publications on the applications of AI for COVID-19 were obtained from the WoS database with the query: TS=("covid-19") AND (TS=("machine learning") OR TS=("artificial intelligence") OR ALL=("data science") OR ALL=("data mining")). All the major keywords which lie in the framework of AI were included. We 4 Analysis, results, and discussions

Citation analysis
Out of the obtained 2,434 scientific publications, each was assigned into slabs depending upon the number of citations. A total of eight such slabs of citations were created: "greater than equal to 0", "greater than equal to 10", "greater than equal to 25", "greater than equal to 50", "greater than equal to 100", "greater than equal to 200", "greater than equal to 400", and "greater than equal to 500". On distributing the publications based on their citation counts to these slabs, we see that only one publication obtained more than or equal to 500 citations, which accounts for only 0.04% of the total publications. Furthermore, only one publication so far has garnered less than 500 but more than or equal to 400 citations. The highest percentage share of publications, i.e., 85.54% publications lie in the slab of publications with greater than or equal to 0 citations. This implies that there are 2082 publications out of the total group, which either have citations more than or equal to 1 and less than 10 or remain uncited so far. This implies that due to the recent nature of AI centric publications related to COVID-19, most of the research has not yet garnered citations. This may also be due to the high rate of publications, which makes it difficult for new researchers to cite. The citation structure of all the publications is depicted in Table 1.

Document types as per WoS database
The research publications can be broadly classified into various types such as articles, corrections, data papers, early access articles, editorial materials, letters, news items, articles in proceedings, and reviews. Based on these categories, the obtained records of publications were classified, as shown in Table 2. It is observed that the highest number of publications (2,021) were classified as articles, followed by 257 and 210 publications classified as reviews and early access, respectively. Apart from these, editorial material, letters, meeting abstract, correction, and data papers consist of 95, 23, 23, 13, and 11 publications, respectively. Two publications each were classified as book chapters, news items, and proceedings papers. Corresponding to this analysis, approximately 83% of the total publications lie in the category of articles, while 210 still lie in the early access. This once again suggests that the research interest on COVID-19, focused on AI, is increasing as time escalates. Hence, more publications have been submitted in recent days.

Analysis of top papers on COVID-19
In this section, we analyze the top papers published on COVID-19 with focus on AI. Table 3 depicts the top 50 papers ranked based on total citation (TC) count. The table also depicts the title, authors, journal published in (Source Title), publication month, influential AI subgroups etc. associated with each publication. It can be seen that the top ranked paper, titled "Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal" [90] was published in British Medical Journal in April, 2020. This paper gave an overview of the ML prediction models on the early study of COVID-19 which could help in assisting clinical decisions. Recall from Section 4-A that this is the only paper to have garnered more than 500 citations, accounting for 0.04% of the total citations obtained by all the publications considered together. The article titled "The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users" [49] was published in International Journal of Environmental Research and Public Health (IJERPH) in March 2020 and ranks second in terms of citations in the top 50 papers list globally with 405 citations. It is noticeable that the top ten papers on the basis of TC have been published within the time period March 2020 -August 2020. Another analysis of TC for top 50 papers reveals interesting findings. In Fig. 2, a frequency histogram of publications according to TC is depicted. To obtain this figure, the entire range of TC was divided into 10 bins. Each bar against the bin depicts the number of publications falling in that bin (having obtained corresponding number of citations). Analyzing this frequency histogram demonstrates that most publications in the top 50 list have garnered citations between 60-100. The number of publications decrease as number of citations increase in the bins. Figure 3 is another frequency distribution histogram depicting the relationship of the number of authors with the impact of the publication. In this figure, the X-axis denotes the bins of number of authors, while the bars depict the number of papers in that bin. We decided upon the same 10 bins for this analysis. The highest number of publications in the top 50 list have 2 to 9 authors. The number of publications decreases as the number of authors within each publication increases. It is very interesting to note that there is one paper in the top 50 papers list where the number of authors is 40, which is indicative of an interdisciplinary research work. * Percentages in the last column of the table add up to more than 100. This is because one publication may be classified into more than one publication type

Technical analysis of top articles based on AI centric COVID-19 research
Due to the significant applicative capabilities of AI, it is crucial to dive deep into its impact on the research of COVID-19. In this section, specifics of different fields and types of AI are discussed, with respect to the top-50 research articles provided in Table 3. To begin with the topmost highly cited research article, 'Prediction models for diagnosis and prognosis of COVID-19: Systematic review and critical appraisal', [90] studied published articles and preprints of prediction models of COVID-19, meant for diagnosis, prognosis as well as detection of people in general population with increased risk of infection. In this article, AI, specifically through a text analysis tool, was utilized to prioritize research materials relevant to the study. In the next article from this list, [49], posts from Weibo, a Chinese micro-blogging website were considered to study the psychological impacts of COVID-19 on the former's users. This work used the Online Ecological Recognition (EOR), based on the broad category of ML predictive models. The article titled 'Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions', [92] ranked third highest in terms of citations, developed a simple neural network structure based on long-short term memory (LSTM) which predicted peak of infection in mainland China. Automatic detection of COVID-19 through binary and multi-class classifications was performed in [60]. The article [12], with the fifth-highest citations, employed AI as a tool for primary filtration of gene data through doublet cell detection. Subsequently, a classification deep neural net called the COVNet was proposed in [48], to identify COVID-19 and community-acquired pneumonia infections from no infections using volumetric chest CT scans. The article 'Proteomic and Metabolomic Characterization of COVID-19 Patient Sera' [69] presented an ML-based random forest classifier to identify patients with severe COVID-19 infections, following their protein and metabolite characteristics. With interpretability as a highlighting concern for AI models, authors of [91] developed a discriminative ML model for the classification of the most important biomarkers of patient mortality, concerning the COVID-19 disease. The XGBoost model was considered for this task. In [56], the authors studied CT scans for presented patients using convolutional neural networks to learn specific characteristics. Since these scans presented normal radiological findings in early stages of infection multilayer perceptron (MLP) classifiers were put to use for classification of patients using more clinical information. The article with tenth highest number of citations [77], reviewed research articles to identify most significant seven AI applications pertaining to detection of COVID-19.
In the subsequent data, multiple review articles [4,14,16,23,41,55] studied either the publication landscape, or the impact on COVID-19 responses due to AI and/or other digital technologies such as blockchain, IOT etc. Furthermore, a total of four [6,59,72,75] articles within the top 10-25 articles with highest citations, dealt with the detection of COVID-19 in patients through segmentation and classification on chest X-Ray images. It is interesting to note that most of such models are deep learning models, hence indicating the prowess of this AI trajectory in the detection of COVID-19. To top these off, four [17,38,39,42] articles in the top 10-25 presented data analysis and modelling results and frameworks, few of which dealt with big data analysis, susceptible-exposed-infected-recovered (SEIR) models and mobility networks. Apart from these, only one LSTM based forecasting model for COVID-19 [21] managed to rank 25th in garnering citations.
From the above analysis, it is easy to observe that research on classification models for detecting the presence and/or absence of COVID-19 in patients is quite prominent and also garners the most interest. Amongst these, DL models reign due to their highly accurate results, given a good amount of data. A review of the impact of AI-centric research on COVID-19 is a second favorite amongst researchers to study and present the effect that COVID-19 has had on the world in different ways. Data analysis and various methods of modelling the same have also grasped the interest of researchers to imply essential results through the study of data obtained from multitudes of resources and in diverse modalities.

Research areas of publications
In Table 4, we list the top 50 research areas of publications obtained from WoS. The top 5 research areas include "Computer Science", "Engineering", "Science Technology Other Topics", "Medical Informatics", and "Health Care Sciences Services" with 547, 334, 230, 210, and 201 number of publications in each research area, respectively. It can be seen clearly that only the top 5 research areas have more than 200 publication records, collectively accounting for more than 62% share of total publications (TP). The highest % share, i.e.  22.47% of TP is in the research area "Computer Science", which is approximately 11% more than the % share of the "Engineering" research area. While one might think that research related to COVID-19 focused even on AI is being published only in medical related journals, this is not the case in fact. The list of top 50 research areas indicates that AI centric research on COVID-19 has drawn researchers from diverse fields, viz. science and technology, engineering, chemistry, social science, psychology etc. The minimum number of publications in top 50 is atleast 10.

Analysis of journals
The destination journal of a publication is one of the most important factors while analyzing any publication data. Journals with high TP are the most productive journals, whereas the ones with the highest TC are the most influential journals. Tables 5 and 6, respectively, list the top 20 most productive and most influential journals with AI centric publications related COVID-19.
The "Journal of Medical Internet Research" is the most productive journal with 90 publications, followed by "IEEE Access" with 84 publications. Other journals among the top 5 ranks are "International Journal of Environmental Research and Public Health", "Scientific Reports", and "Computers Materials & Continua" possessing 53, 52 and 44 publications, respectively. The journals in Table 5 consists of a mix of domains from science, medical and engineering. Qualitatively, though the journals may appear belonging to several domains, the scope of each of these journals targets the applicability of AI and ML approaches.
The two journals with highest citations per paper (CPP) in the list are "Computers in Biology and Medicine" (CIBM) and "Chaos Solitons & Fractals" (CSF) with CPP of 30.95 and 22.40. Both journals publish work pertaining to computational biology in common, and thus, they support the publications on COVID-19, which addressed AI, ML, and data analytics. Despite publishing only 19 and 25 papers, these received decent citations of 588 and 560, respectively. Furthermore, the highly cited papers from CIBM targeted the use of DL methods for the intrinsic analysis on COVID-19. These papers were: "Automated detection of COVID-19 cases using deep neural networks with X-ray images", "Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks", and "COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches" etc. The highly cited papers from CSF targeted time series forecasting of COVID-19 and some thorough review on the Applications of machine learning and artificial intelligence for COVID-19.
In Table 6 the top 20 highly influential journals are listed, which garnered the most attention from other publications. The top two journals are "International Journal of Environmental Research and Public Health" (TC = 765) and "IEEE Access" (TC = 647). CIBM and CSF, both journals with the highest CPP above, rank third and fourth in the list of most influential journals. The top seven journals in this list account for more than 300 citations which, apart from the top four, includes "Radiology", "Journal of Medical Internet Research", and "PLOS One" with TC of 451, 429, and 309, respectively.
The highest CPP of 52.80 is received by "IEEE Transactions on Medical Imaging" which targets the application of AI and ML in the medical image processing. It is followed by "Radiology" and CIBM with CPP of 41 and 30.95, respectively. It is observed that there are many open access journals in both the lists as it was the need of the hour to publish articles as soon as possible and within the reach of every academician and scientist. It also points out the extensive amount of funding invested for such research. The funding details are discussed at length in Section 4.12. Figure 4 depicts the co-citation mapping between the journals that have published AI centric papers related to COVID-19. Since there were 30,911 sources, we considered only those journals which have received at least 100 citations. This resulted in a total of 116 journals. Co-citation analysis for a journal considers the total number of citations and compute how many times a journal paper has been cited in another journal. From Fig. 5, it is observed that "IEEE Access", "Radiology", "Lancet", "PLOS One", and "Nature" are the largest nodes indicating that they have been cited more by the other journals for COVID-19 research related to AI. The clusters distinguished by different colors indicate more citations between them. For example, "Arxiv preprint" has articles which have cited more papers from IEEE access, hence they are in same cluster with strong link strength.

Analysis of authors
Akin to journals, the top authors of publications related to COVID-19 focused on AI are categorized as the top 20 most productive authors in Table 7. The difference in the number of publications among these top 20 authors is not much significant. Al-Turjman tops this list with a TP of 9, followed by Duong and Hassanien with a TP of 8 and 7, respectively. Then there are 8 authors with 6 publications each. Rest of the authors in the list have 5 papers. Interestingly, Acharya has the highest CPP of 92.40 with only 5 papers. On the other hand, Ittamalla with 6 papers has the lowest CPP of 0.50. This is because Acharya's publication has targeted AI and medical informatics which were the centre of attraction among researchers during pandemic. Table 8 shows the list of top 20 most influential authors, i.e., authors ranked based on their TC. First rank is obtained by Xue with a TC of 466 with only 6 papers. Other than Xue, there are three other authors with more than 400 citations. These authors are Acharya (TC = 462), Zhu (TC = 456), and Li (TC = 424). Then there are 10 authors with more than 100 citations. All the authors in this list have published atleast 3 papers. Another very interesting observation that can be made here is that, if we compare Tables 7 and 8, we find that only 8 authors from the most productive list find their place in the most influential authors. Also, the 3 topmost influential authors also fall in the list of top 20 most productive authors, which indicates that their quality work is well received by the research community.
The co-citation amongst authors describes the citing relationship between two authors i.e. the number of times an author is cited by the others. The connections resulting from a cocitation versus authors mapping is presented in Fig. 5. For better visualization, the minimum number of citations of each author has been kept at 50 which among 57,770, resulted in only 125 authors. The node corresponding to the WHO is the largest indicating that the articles published by them are cited the most by all the authors, which is very relevant and justified. Authors in the same color cluster indicate the strong co-citation among them. Li and Wang gathered much attention with respect to the red color clusters. Similarly, Huang is another author from green cluster who has been cited most.

Analysis of institutions
Institutions play a key role in the promotion of scientific research. In the case of AI centric research on COVID-19, the top 20 most productive institutions are given in Table 9. This also makes University of Oxford as the most influential institution with the highest TC of 654, while Huazhong University of Science and Technology has the second highest TC amounting to 650, closely trailed by Medical University of Vienna with TC of 649. This can be viewed from Table 10, where the top 20 most influential institutions for AI centric research on COVID-19 are given. There are total of 12 institutions with more than 500 citations which is a great achievement considering almost 1 year of publication. The highest CPP of 129.80 is attained by Medical University of Vienna, which is remarkable to achieve through just 5 publication within few months. Even the lowest citation count in this list is 366, obtained by Icahn School of Medicine at Mount Sinai, emphasizing that there is a huge number of works that are being published in AI centric research on COVID-19.

Analysis of countries
The first case of COVID-19 was reported in China in December, 2019. Since then, strict isolation measures were put into practice to prevent the spread of this disease. However, reports on people being infected by COVID-19 were soon registered in various countries such as Thailand, Korea, Japan etc. [80]. Worldometer [87] listed 222 countries/regions as being For ease of analysis, these countries were divided into two lists of top 20 most productive and most influential countries, as shown in Tables 11 and 12, respectively. Referring to both these tables, it can be observed that USA, China, United Kingdom, and India retain the top 4 ranks. The order in which these countries are ranked based on their TP is USA, China, India, and United Kingdom with a TP count of 721, 326, 344 and 236, respectively. Notably, second ranked China has less than around 50% of total publications than USA. In terms of TC, USA, USA, England, and India obtain counts of 4750, 4130, 2787 and 1803, respectively. The highest CPP of 47.75% is obtained by Belgium with TC of 955 in just 20 publications. Furthermore, we also observe that Asia as a continent has the most number of productive and influential countries in the top 20 list. Specifically, six countries out of the 20 most productive countries lie in Asia. The second spot is taken up by USA.

Bibliographic coupling amongst various data
Bibliographic coupling is a measure that indicates the possibility of similarity between works done by two entities [53]. It is intuitively defined as the number of times more than one entity cites one common entity. These entities may be institutions, countries, authors etc. We have considered all such different entities and obtained results for each of them.  network. The size of the bubbles indicates those authors, publications by whom are the most cited in their cluster. For example, publications by Kuhl have been cited highly when compared to other authors in the green cluster. Similarly, Mahmud (blue cluster), Magazzino (voilet cluster), Ittamalla (yellow cluster) and Al-turjman (red cluster) are the main players in their respective clusters. Each cluster is based on of high frequency citations amongst the authors within that cluster. Furthermore, the curves linking the nodes in the clusters have different width. Higher width of the curves demonstrates more matches in the references of the publications by the two authors.

Bibliographic coupling amongst countries
The bibliographic coupling network amongst the countries depicts the similarities in references between publications by two countries, as shown in Fig. 7. It can be clearly observed that the main players in this network are USA and China (green cluster), Canada and Italy (yellow cluster), India and Saudi Arabia (red cluster), Germany (blue cluster), Australia (violet cluster) etc. Curves connecting nodes corresponding to Switzerland and Spain, USA and Canada, India and Turkey etc. possess the most width indicating the similarity in the references of their works.

Bibliographic coupling amongst institutions
A dense network of bibliographic relationships between institutions worldwide is shown in Fig. 8. Huazhong University of Science and Technology (blue cluster), Harvard Medical School (red cluster), and King Saud University, University of Toronto, and King Abdulaziz University (green cluster) can be identified as the main institutions in each cluster within the network that receive high citations within their respective clusters. The high density of connections indicates that the citation network amongst institutions is extremely diverse.

Bibliographic coupling amongst journals
Various clusters can be observed from the bibliographic coupling network between journals as shown in Fig. 9. Amongst these the "Journal of Medical Internet Research" and "PLOS One" (red cluster), "IEEE Access" (green cluster), "Diagnostics" (blue cluster), "Journal of Intelligent & Fuzzy Systems" (yellow cluster) etc. are the main entities in their respective clusters of the entire network. Journals such as "IEEE Access", "Journal of Medical Internet Research", It is worth noticing that the journals that do not appear in any clusters indicates that these journals, being important journals within their respective clusters, do not share any common references with any other journal in the network.  Figure 10 shows the network of co-occurrence of different keywords within a publication, used by various authors. It can be seen that "COVID-19" is the highest occurring keyword in all the publications overall. Width of the curves connecting the nodes in the network demonstrate the frequency of these keywords occurring together within publications. For example, "COVID-19", "big data", and "artificial intelligence" have occurred more frequently together when compared to "COVID-19" and "statistics" or "molecular docking" occurring together. Other important keywords in this network are: "machine learning" (not visible in the figure, due to the software's limitations. It is under the COVID-19 node), "neural network", "feature extraction", "classification", "computed tomography" etc.

Funding agencies
In addition to the aforementioned results, the agencies that have funded most publications worldwide were also identified during our analysis. This study presented the geographical locations where agencies were generously funding to deal with the pandemic using the AI centric approaches. The top 50 funding agencies worldwide were identified and ranked based on number of publication records funded by them. This is shown in

Discussion and conclusion
While writing this paper, the entire world is still under the grip of the deadly COVID-19, strying to recover from its devastating impacts during the last two years. To prevent further spread and to gather more insight into the behavior and effect of this deadly virus, researchers from all over the globe, worked desperately to come up with possible vaccines. Biomedical and computational sciences, along with AI and ML have together made this happen in a decent amount of time. This has resulted in a huge volume of scientific publication data, analyzing which, useful insights can be obtained. In this paper for the first time, we have performed an extensive analysis (both qualitative and quantitative) of the AI centric publication related to COVID-19. This analysis has revealed multiple key findings. These are given below: A. Detailed key findings Within the last two years, the research on COVID-19 with focus on AI has shown a significant growth. Overall, the papers have gathered 15,607 citations in just 2,434 papers. The papers published in April 2020 have obtained the highest citations till the time this paper is being written, with the count of 542. Notably, there are 85.54% publications which have either no or less than 10 citations count. This is naturally due to the continuous flow of work in the literature. It is anticipated that the citation counts of the quality work will increase with time.
Apart from this, the maximum number of the papers that have been published are articles, among which many are open access. This is acceptable in the sense that the review process pertaining to these is quicker, and such research work is available to the research community for free. Analyzing the top 50 papers based on citation reveals that most of these have obtained citations between 60 and 100. This reveals the premature status of the AI centric research on COVID-19. Interestingly, there are two papers in the top 50 list which have been published in January and August of 2021 and have gathered significant citations of 124 and 70, respectively. It was also revealed that classification using DL models garnered the highest interest amongst both researchers and readers.
Amongst the research areas, 'Computer Science' and 'Engineering' remain at the top, having contributed the most to AI centric research to COVID-19. Similarly, the Journal of Medical Internet Research, IEEE Access, and the International Journal of Environmental Research and Public Health are among the top 3 productive journals for research on AI for COVID-19. On the other hand, the International Journal of Environmental Research and Public Health ranks highest in the most influential journal list, while IEEE Access stands at second spot.
Considering authors, Al-Turjman published the most number of papers while, Xue obtained the highest citations for research related to AI for COVID-19. Additionally, Harvard Medical School, Huazhong University of Science and Technology, and Chinese Academy of Sciences are the top institutions publishing on COVID-19 focused on AI. However, the University of Oxford stands at the top of the most influential institutions list.
Country-wise analysis of AI centric research on COVID-19 reveals the top contributors to be USA, China, India, and England. However, Asia is the top continent in terms of both citations and publications. It was also revealed that "COVID-19" is the highest used keyword in all the publications considered. It was coupled the most with keywords "artificial intelligence" and "machine learning". Also, the United States Department of Health Human Services is the topmost funding agency, which sponsored the research for the highest number of publications.

B. General observations
The current publication data available on AI centric research on COVID-19 is quite significant and growing gradually. Now, the world is in the state of rehabilitation from the havoc of the pandemic. Many AI and ML approaches have been implemented to the COVID data for the data analytics and predictions. Our investigation has revealed some very useful insights related to the AI centric publications on COVID-19. Although most of the research on this topic is recent, publications, in general, have been cited significantly as compared with other matured fields. These numbers will increase over time because AI-centric research on COVID-19 and related studies are still growing in numbers.
It is interesting to note how the USA and India, being two of the worst hit countries due to COVID-19 in the pandemic, has produced the highest numbers of publications, when considered country-wise. Even though in terms of continents, Asia tops the list on production of papers while also obtaining the highest number of citations. This is an indicator of the fact that multiple countries within Asia are devoted to conducting AI centric research on COVID-19.
As a future work, we shall include various other indexing platforms such as Scopus, Google scholar and DBLP. Furthermore, we shall extend the work on several factors including institution based bibliometric analysis, country-based analyses, etc. In doing so, we will also target to develop a dynamic platform where we can regularly update the statistics related to the AI centric scientific research on COVID-19.
Funding Open Access funding provided by University of Jyväskylä (JYU).
Data availability Data available on request from the authors.
Code availability Code available on request from the authors.

Conflicts of interest/Competing interests There is no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .